Recent genetic studies have increasingly shown that interstitial deletions are common in patients with cancers, such as lung cancer and head and neck squamous cell carcinoma, and psychiatric disorders, such as autism and schizophrenia, suggesting that genomic deletions play an important role in the genetic basis of complex traits in the human genome. However, the association between genomic deletions and common, complex diseases has not yet been systematically investigated in gene mapping studies. Whole-genome studies of genomic deletions have been performed extensively over the past few years. Many of these studies focus on investigating genetic variations in non-diseased individuals and can provide fundamental resource of baseline information for the study of human disease and genomic evolution. However, assessing these effects and associating them with susceptibility to common, complex diseases remain challenging. The central theme of this proposal is to develop statistical approaches to be used to perform genome-wide deletion scans in case-control studies. Our proposed methods are designed to be used with high-density SNP genotypes to detect deletions in large-scale or whole-genome genetic studies. As more and more high-density SNP genotype data on a variety of common, complex diseases will be available from genome-wide association studies, development of sophisticated statistical approaches is especially relevant and novel. Two SNP-based statistical approaches will be developed. The first method is used to test the presence of deletions associated with disease on each of contiguous SNP loci along a chromosomal region for SNP-by-SNP analyses. The second is designed to utilize evidence from multiple adjacent SNPs combined to assess the statistical significance of disease-associated deletions in cases compared with in controls using cluster-based approaches. We propose to use simulation-based approaches to quantitatively determine the statistical sensitivity and power of the proposed methods, adjusting for deletion length, deletion prevalence, deletion penetrance, SNP density, and magnitude of linkage disequilibrium. The newly developed statistical approaches will be used to perform genome-wide detection of deletions in associated with lung cancer. The NCI-funded project "The Ecogenetics Study of Lung Cancer" (R01 CA 55769, PI: MR Spitz) provides the SNP genotype data from a recently completed genome-wide association study of lung cancer.